Determining water mass flow control strategies for a turbocharged SI engine using a two-stage calculation method

TL;DR

This paper introduces a two-stage calculation method combining simplified and detailed models to optimize water flow control strategies for temperature regulation in turbocharged SI engines, improving efficiency and robustness.

Contribution

It presents a novel two-stage modeling approach for water flow control in engines, integrating simplified and detailed simulations for optimized strategy determination.

Findings

Engine temperatures can be effectively regulated over a wide range.

The two-stage method is efficient and yields consistent results across models.

Different control strategies like feed forward and feedback are compared successfully.

Abstract

Reduction of heat and friction losses is a proven approach to increase the engine efficiency. Therefore, and due to a stabilized, robust combustion, a specific adjustment of component temperatures is desirable in highly transient conditions. In this paper, a turbocharged SI engine is investigated numerically concerning the potential regulation of temperatures, including heat fluxes, only by controlling the water mass flow rate. Using two independent models, a simplified lumped capacity model and a detailed three-dimensional CFD-CHT simulation, an efficient, two-stage calculation method is suggested for an optimized determination of control strategies and their parameters. This complements existing published works which usually control more than one parameter, but use one model. Different control strategies, like feed forward or feedback controllers, are proposed and compared. In addition, a more holistic approach is presented performing a Monte Carlo simulation which evaluates temperatures, as well as hydraulic pumping losses. Using expedient control strategies and parameters, it could be shown that the engine temperatures can be effectively regulated within a wide range. The two different models show partly similar results, and the efficient, two-stage optimization method has proven its worth. However, there are some significant differences between simplified and detailed modelling which are worth mentioning.